n8n-workflows/workflows/FU3MrLkaTHmfdG4n_Hugging_Face__to_Notion.json
2025-05-14 11:58:29 +03:00

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{
"id": "FU3MrLkaTHmfdG4n",
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"templateCredsSetupCompleted": true
},
"name": "Hugging Face to Notion",
"tags": [],
"nodes": [
{
"id": "32d5bfee-97f1-4e92-b62e-d09bdd9c3821",
"name": "Schedule Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
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],
"parameters": {
"rule": {
"interval": [
{
"field": "weeks",
"triggerAtDay": [
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2,
3,
4,
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"triggerAtHour": 8
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"parameters": {
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"leftValue": "={{ $json }}",
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"fieldToSplitOut": "url, url"
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{
"id": "08dd3f15-2030-48f2-ab0f-f85f797268e1",
"name": "Request Hugging Face Paper",
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"parameters": {
"url": "https://huggingface.co/papers",
"options": {},
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"queryParameters": {
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"parameters": {
"filters": {
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{
"key": "URL|url",
"urlValue": "={{ 'https://huggingface.co'+$json.url }}",
"condition": "equals"
}
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"operation": "getAll",
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"mode": "list",
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"credentials": {
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},
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{
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"name": "Request Hugging Face Paper Detail",
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"position": [
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],
"parameters": {
"url": "={{ 'https://huggingface.co'+$('Split Out').item.json.url }}",
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{
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"type": "@n8n/n8n-nodes-langchain.openAi",
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],
"parameters": {
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"mode": "list",
"value": "gpt-4o-2024-11-20",
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},
"options": {},
"messages": {
"values": [
{
"role": "system",
"content": "Extract the following key details from the paper abstract:\n\nCore Introduction: Summarize the main contributions and objectives of the paper, highlighting its innovations and significance.\nKeyword Extraction: List 2-5 keywords that best represent the research direction and techniques of the paper.\nKey Data and Results: Extract important performance metrics, comparison results, and the paper's advantages over other studies.\nTechnical Details: Provide a brief overview of the methods, optimization techniques, and datasets mentioned in the paper.\nClassification: Assign an appropriate academic classification based on the content of the paper.\n\n\nOutput as json\n{\n \"Core_Introduction\": \"PaSa is an advanced Paper Search agent powered by large language models that can autonomously perform a series of decisions (including invoking search tools, reading papers, and selecting relevant references) to provide comprehensive and accurate results for complex academic queries.\",\n \"Keywords\": [\n \"Paper Search Agent\",\n \"Large Language Models\",\n \"Reinforcement Learning\",\n \"Academic Queries\",\n \"Performance Benchmarking\"\n ],\n \"Data_and_Results\": \"PaSa outperforms existing baselines (such as Google, GPT-4, chatGPT) in tests using AutoScholarQuery (35k academic queries) and RealScholarQuery (real-world academic queries). For example, PaSa-7B exceeds Google with GPT-4o by 37.78% in recall@20 and 39.90% in recall@50.\",\n \"Technical_Details\": \"PaSa is optimized using reinforcement learning with the AutoScholarQuery synthetic dataset, demonstrating superior performance in multiple benchmarks.\",\n \"Classification\": [\n \"Artificial Intelligence (AI)\",\n \"Academic Search and Information Retrieval\",\n \"Natural Language Processing (NLP)\",\n \"Reinforcement Learning\"\n ]\n}\n```"
},
{
"content": "={{ $json.abstract }}"
}
]
},
"jsonOutput": true
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"parameters": {
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"cachedResultName": "huggingface-abstract"
},
"propertiesUi": {
"propertyValues": [
{
"key": "URL|url",
"urlValue": "={{ 'https://huggingface.co'+$('Split Out').item.json.url }}"
},
{
"key": "title|title",
"title": "={{ $('Extract Hugging Face Paper Abstract').item.json.title }}"
},
{
"key": "abstract|rich_text",
"textContent": "={{ $('Extract Hugging Face Paper Abstract').item.json.abstract.substring(0,2000) }}"
},
{
"key": "scrap-date|date",
"date": "={{ $today.format('yyyy-MM-dd') }}",
"includeTime": false
},
{
"key": "Classification|rich_text",
"textContent": "={{ $json.message.content.Classification.join(',') }}"
},
{
"key": "Technical_Details|rich_text",
"textContent": "={{ $json.message.content.Technical_Details }}"
},
{
"key": "Data_and_Results|rich_text",
"textContent": "={{ $json.message.content.Data_and_Results }}"
},
{
"key": "keywords|rich_text",
"textContent": "={{ $json.message.content.Keywords.join(',') }}"
},
{
"key": "Core Introduction|rich_text",
"textContent": "={{ $json.message.content.Core_Introduction }}"
}
]
}
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